Few attempts have been made to combine the structured part of KGs with entity descriptions to learn KGE models. Among these models, DKRL [1]}, MKBE [2]}, and Jointly [3]} use neural network encoders (either CNN or LSTM) to represent entity descriptions. The other models are SSP [4]} and LiteralE [5]} which rely on document embedding approaches to get representations for entity descriptions. In DKRL, CNN is used to encode entity descriptions using word embeddings as an input.
MKBE, same as in DKRL, uses CNN to encode textual descriptions of entities. The descriptions that are used in both DKRL and MKBE are provided in only one language (English).
